Tag: Molecular Dynamics

3 entries

ATOM: A Pretrained Neural Operator for Multitask Molecular Dynamics

Original Research Paper
ICLR 2026 paper proposing ATOM, a quasi-equivariant pretrained transformer neural operator that requires no explicit molecular graph and uses temporal attention for parallel decoding of multiple future states; curates TG80 (80 compounds, 2.5M fs trajectories) and demonstrates zero-shot generalization on MD17/RMD17/MD22.
arXiv preprint (copyright held by authors) Neural OperatorMolecular DynamicsTransformer

MACE-MP-0: A Universal Machine-Learned Interatomic Potential

Molecular Dynamics / ML Interatomic Potential
A general-purpose machine-learned interatomic potential built on an equivariant graph neural network, trained on 150k public inorganic crystals, that runs stable molecular dynamics out of the box for solids, liquids, gases and reactions, and can be fine-tuned to ab initio accuracy with only a handful of configurations.
MIT (code) / arXiv preprint Molecular DynamicsML PotentialEquivariant GNN

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